Machine learning for predicting severe dengue in Puerto Rico

Machine learning for predicting severe dengue in Puerto Rico

Abstract Background Distinguishing between non-severe and severe dengue is crucial for timely intervention and reducing morbidity and mortality. World Health Organization (WHO)-recommended warning signs offer a practical approach for clinicians but have limited sensitivity and specificity. This stud...

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Bibliographic Details
Main Authors: Zachary J. Madewell, Dania M. Rodriguez, Maile B. Thayer, Vanessa Rivera-Amill, Gabriela Paz-Bailey, Laura E. Adams, Joshua M. Wong
Format: Article
Language:English
Published: BMC 2025-02-01
Series:Infectious Diseases of Poverty
Subjects:
Dengue
Ensemble learning
Gradient boosting
Feature importance
Clinical decision support
Caribbean
Online Access:https://doi.org/10.1186/s40249-025-01273-0
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https://doi.org/10.1186/s40249-025-01273-0

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